list_experiments

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full behavioral burden. It describes the return structure (fields: experiment_id, constraint, dataset, seed, logic, epochs) and explains constraint filter behavior (accepts class name or folder name). This adds meaningful transparency beyond just 'list runs'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences, each serving a purpose: stating the action, detailing the output, and providing usage guidance. No redundancy or unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the presence of an output schema, the description covers all essential aspects: what is returned, the filter format, and the inter-tool dependency. It is complete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides specific detail for the constraint parameter (accepts class name or folder name) but does not describe the logic or dataset parameters beyond being optional filters. Some added value, but incomplete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the verb 'List', the resource 'completed experiment runs', and notes optional filters. It differentiates from siblings by stating that this tool returns experiment_ids used by other experiment tools, making its role distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs to call this tool first because every other experiment tool takes an experiment_id from here. This provides clear context for usage, though it does not mention when not to use it or name specific alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.